Can a pre-trained EEG neonatal model be used for seizure detection in pediatrics?

Epileptic seizures affect more than 50 million population worldwide. Automated methods for seizure detection from EEG can help detect seizures faster and reduce the diagnostic delay. While there are many deep learning solutions to seizure detection the transferability of the learnt representation across age groups has not been studied. In this paper, we first evaluate the performance of the state-of-the-art neonatal seizure detection model on a publicly available pediatric CHB-MIT EEG dataset. The obtained results are then contrasted with the performance of fine-tuned neonatal model on pediatric data. The developed patient-independent model achieved an average AUC score of 91.62% on the CHB-MIT dataset. This is the first study to assess whether a universal model is realizable for different age groups.